NDLI: Parallel in situ indexing for data-intensive computing

Content Provider	IEEE Xplore Digital Library
Author	Jinoh Kim Abbasi, H. Chacon, L. Docan, C. Klasky, S. Qing Liu Podhorszki, N. Shoshani, A. Kesheng Wu
Copyright Year	2011
Description	Author affiliation: University of Tennessee, Knoxville, USA (Abbasi, H.) \|\| Lawrence Berkeley National Lab, USA (Jinoh Kim; Shoshani, A.; Kesheng Wu) \|\| Oak Ridge National Lab, USA (Chacon, L.; Klasky, S.; Qing Liu; Podhorszki, N.) \|\| Rutgers University, USA (Docan, C.)
Abstract	As computing power increases exponentially, vast amount of data is created by many scientific research activities. However, the bandwidth for storing the data to disks and reading the data from disks has been improving at a much slower pace. These two trends produce an ever-widening data access gap. Our work brings together two distinct technologies to address this data access issue: indexing and in situ processing. From decades of database research literature, we know that indexing is an effective way to address the data access issue, particularly for accessing relatively small fraction of data records. As data sets increase in sizes, more and more analysts need to use selective data access, which makes indexing an even more important for improving data access. The challenge is that most implementations of indexing technology are embedded in large database management systems (DBMS), but most scientific datasets are not managed by any DBMS. In this work, we choose to include indexes with the scientific data instead of requiring the data to be loaded into a DBMS.We use compressed bitmap indexes from the FastBit software which are known to be highly effective for query-intensive workloads common to scientific data analysis. To use the indexes, we need to build them first. The index building procedure needs to access the whole data set and may also require a significant amount of compute time. In this work, we adapt the in situ processing technology to generate the indexes, thus removing the need of reading data from disks and to build indexes in parallel. The in situ data processing system used is ADIOS, a middleware for high-performance I/O. Our experimental results show that the indexes can improve the data access time up to 200 times depending on the fraction of data selected, and using in situ data processing system can effectively reduce the time needed to create the indexes, up to 10 times with our in situ technique when using identical parallel settings.
Starting Page	65
Ending Page	72
File Size	1240869
Page Count	8
File Format	PDF
ISBN	9781467301565
e-ISBN	9781467301558
DOI	10.1109/LDAV.2011.6092319
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Buildings Parallel processing Data processing Data models Indexing
Content Type	Text
Resource Type	Article

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